Senior Analytics Engineer (Platform - Financial Analytics)

Ready to be pushed beyond what you think you're capable of?

At Coinbase, our mission is to increase economic freedom in the world. It's a massive, ambitious opportunity that demands the best of us, every day, as we build the emerging onchain platform - and with it, the future global financial system.

To achieve our mission, we're seeking a very specific candidate. We want someone who is passionate about our mission and who believes in the power of crypto and blockchain technology to update the financial system. We want someone who is eager to leave their mark on the world, who relishes the pressure and privilege of working with high caliber colleagues, and who actively seeks feedback to keep leveling up. We want someone who will run towards, not away from, solving the company's hardest problems.

Our https://www.coinbase.com/mission is intense and isn't for everyone. But if you want to build the future alongside others who excel in their disciplines and expect the same from you, there's no better place to be.

While many roles at Coinbase are remote-first, we are not remote-only. In-person participation is required throughout the year. Team and company-wide offsites are held multiple times annually to foster collaboration, connection, and alignment. Attendance is expected and fully supported.

• About the Team*

The Finance Analytics team bridges the gap between data engineering, data science, and business analytics by building scalable, impactful data solutions that empower Finance, Accounting/Controllership, and Treasury stakeholders to make data-driven decisions. We bring deep domain knowledge spanning accounting, business controllership, SOX compliance, and internal audit - ensuring our pipelines, data models, and certified financial datasets meet the rigor and traceability demands of a regulated financial environment.

This role supports the data layer efforts that underpin these datasets and the controls/observability needed to keep them reliable. The team partners closely with Finance, Accounting, Treasury, and Product Engineering to ensure financial data is complete, reconciled, and audit-ready - supporting core workflows like month-end close, safeguarding, and reporting at scale.

• What You'll Be Doing:*

This is a hybrid Data Engineer/Data Scientist/Business Analyst role that has the expertise to understand data flows end to end, and the engineering toolkit to extract the most value out of it indirectly (building tables) or directly (solving problems, delivering insights).

• Be the expert: Quickly build subject matter expertise in a specific business area and data domain. Understand the data flows from creation, ingestion, transformation, and delivery.
• Step into a new line of business and work with Engineering and Product partners to deliver first data pipelines and insights.
• Communicate with engineering teams to fix data gaps for downstream data users.
• Take initiative and accountability for fixing issues anywhere in the stack.
• Perform reconciliation-style validation across sources (internal systems and/or external statements/vendors), identifying discrepancies and driving fixes with upstream owners.
• Examples:
• Generate business value: Interface with stakeholders on data and product teams to deliver the most commercial value from data (directly or indirectly).
• Build curated data models that streamline ledger verification and accounting workflows, helping finance teams accelerate time-to-close for new product launches.
• Leverage deep understanding of the reconciliation engine alongside statistical and data expertise to propose engineering improvements that drive faster execution and higher match accuracy.
• Work with PMs to tie together new x-PG, and x-Product data into one holistic framework to optimize key financing product business metrics.
• Collaborate cross-functionally with Finance/Accounting to translate requirements into durable data models, and with upstream engineering teams to improve source data contracts.
• Examples:
• Focus on outcomes not tools: Use a variety of frameworks and paradigms to identify the best-fit tools to deliver value.
• Develop new abstractions (e.g. UDFs, Python packages, dashboards) to support scalable data workflows/infra.
• Stand up a framework for debugging AI skills/data apps internally, enabling other non-tech stakeholders to quickly add value.
• Use established tools with mastery (e.g. Google Sheets, SQL) to quickly deliver impact when speed is top priority.
• Examples:
• Ensure financial correctness & reliability: Implement strong data quality guarantees (tests, monitoring, alerting, SLAs) and partner with stakeholders to define "done" for financial correctness. Improve reliability and operational excellence for critical pipelines (incident response, retro/action items, performance tuning, cost optimization).
• What We Look For in You:*
• Strong understanding of best practices for designing modular and reusable data models (e.g., star schemas